This series of files compile all analyses done during Chapter 3:

All analyses have been done with R 4.0.2.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

⏪ | 🏠 | ⏩

Sources of activity considered for the analyses:

Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):

Gear Code Years Events Species
Dredge FishDred 2010-2014 21 Mactromeris polynyma
Net FishNet 2010 5 Clupea harengus, Gadus morhua
Trap FishTrap 2010-2015 1061 Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus
Bottom-trawl FishTraw 2013-2014 2 Pandalus borealis

1. Spatial variation of exposure indices

Here, we compute semivariograms for each exposure index (on the whole raster, not only extracted values at the stations).

Aquaculture
## Model selected: Sph
## nugget = 0; sill = 0.00704; range = 7.01955; kappa = 0.5

City
## Model selected: Lin
## nugget = 0.00025; sill = 0.00602; range = 8.57222; kappa = 0.5

Sediment dredging
## Model selected: Exp
## nugget = 0.00021; sill = 0.02042; range = 4.52941; kappa = 0.5

Industry
## Model selected: Sph
## nugget = 1e-04; sill = 0.0072; range = 10.10924; kappa = 0.5

Sewers
## Model selected: Exp
## nugget = 0; sill = 0.03366; range = 43.15003; kappa = 0.5

Shipping
## Model selected: Lin
## nugget = 0; sill = 0.06455; range = 4.27615; kappa = 0.5

Fisheries: Dredge
## Model selected: Lin
## nugget = 0; sill = 0.01019; range = 2.81568; kappa = 0.5

Fisheries: Net
## Model selected: Exp
## nugget = 2e-05; sill = 0.00456; range = 0.70613; kappa = 0.5

Fisheries: Trap
## Model selected: Lin
## nugget = 0.00034; sill = 0.00128; range = 1.12045; kappa = 0.5

Fisheries: Bottom-trawling
## Model selected: Lin
## nugget = 0; sill = 0.03509; range = 3.90932; kappa = 0.5

2. Relationships with abiotic parameters

2.1. Covariation

Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.

⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.

Aquaculture

City

Sediment dredging

Industry

Sewers

Shipping

Fisheries: Dredge

Fisheries: Net

Fisheries: Trap

Fisheries: Bottom-trawling

Cumulative exposure

2.2. Correlation

Correlations have been calculated with Spearman’s rank coefficient.

Correlation coefficients between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture -0.439 0.167 0.477 -0.444 -0.048 -0.688 -0.784 -0.737 -0.668 -0.62 -0.772 -0.766 -0.722 -0.733 0.311 0 -0.029 0.345 0.188
city -0.155 -0.067 0.427 -0.273 -0.096 -0.246 -0.163 -0.171 0.086 -0.004 -0.154 -0.243 -0.167 -0.015 -0.108 -0.036 -0.153 -0.055 0.035
dredging 0.275 -0.084 -0.091 0.103 0.055 0.264 0.19 0.407 0.574 0.649 0.55 0.219 0.324 0.482 -0.215 -0.133 0.049 -0.13 -0.023
industry 0.159 -0.071 -0.016 0.045 0.069 0.176 0.115 0.348 0.514 0.588 0.504 0.157 0.253 0.405 -0.246 -0.115 0.053 -0.198 -0.076
sewers 0.254 -0.037 -0.313 0.268 0.249 0.609 0.581 0.654 0.694 0.591 0.707 0.579 0.689 0.689 -0.353 -0.063 0.021 -0.369 -0.174
shipping 0.456 -0.249 -0.291 0.314 -0.015 0.537 0.504 0.618 0.693 0.677 0.708 0.549 0.576 0.687 -0.19 -0.06 0.022 -0.172 -0.095
fisheries_dredge -0.238 0.068 0.246 -0.241 -0.045 -0.465 -0.458 -0.558 -0.602 -0.648 -0.649 -0.42 -0.474 -0.578 0.334 0.028 -0.084 0.423 0.228
fisheries_net 0.004 -0.055 -0.158 0.119 0.191 0.078 -0.001 0.055 0.033 0.055 0.106 0.01 0.036 0.026 -0.112 -0.137 0.069 -0.035 0.127
fisheries_trap -0.503 0.158 0.422 -0.38 -0.095 -0.444 -0.346 -0.323 -0.318 -0.291 -0.301 -0.353 -0.376 -0.358 0.077 0.182 -0.032 -0.062 -0.169
fisheries_trawl -0.215 0.172 0.088 -0.182 -0.105 -0.237 -0.306 -0.349 -0.451 -0.368 -0.466 -0.313 -0.308 -0.397 0.216 -0.009 -0.038 0.162 0.032
cumulative_exposure 0.241 -0.095 -0.107 0.145 0.035 0.259 0.131 0.283 0.407 0.44 0.375 0.185 0.298 0.373 -0.026 -0.039 0.006 -0.043 -0.096
p-values of correlation test between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture 2.038e-06 0.08404 1.82e-07 1.453e-06 0.6241 1.878e-16 1.123e-23 1.028e-19 2.749e-15 8.733e-13 1.446e-22 4.639e-22 1.119e-18 1.952e-19 0.001047 0.999 0.7653 0.0002525 0.05113
city 0.1087 0.4921 3.982e-06 0.004225 0.3206 0.01043 0.09275 0.07674 0.3781 0.964 0.1118 0.01126 0.08362 0.8744 0.2674 0.7138 0.1134 0.5708 0.7171
dredging 0.004038 0.3876 0.3492 0.2869 0.574 0.005687 0.04861 1.217e-05 8.309e-11 2.962e-14 6.813e-10 0.02309 0.000633 1.283e-07 0.02517 0.171 0.6121 0.1789 0.8096
industry 0.1007 0.465 0.8689 0.6459 0.4811 0.06919 0.2347 0.0002275 1.3e-08 2.144e-11 2.783e-08 0.1043 0.008203 1.389e-05 0.01022 0.236 0.5892 0.03971 0.4341
sewers 0.007974 0.702 0.000962 0.004998 0.009281 2.623e-12 4.439e-11 1.762e-14 8.768e-17 1.703e-11 1.192e-17 5.084e-11 1.659e-16 1.805e-16 0.000176 0.5189 0.8284 8.325e-05 0.07195
shipping 7.165e-07 0.009324 0.002213 0.0009345 0.8743 2.146e-09 2.655e-08 1.041e-12 1.003e-16 9.205e-16 1.105e-17 7.68e-10 7.258e-11 2.359e-16 0.04853 0.5351 0.8202 0.07554 0.3296
fisheries_dredge 0.01314 0.4825 0.01028 0.01193 0.6404 3.925e-07 6.196e-07 3.496e-10 5.797e-12 3.49e-14 2.894e-14 6.176e-06 2.193e-07 5.928e-11 0.0004168 0.7711 0.3857 5.068e-06 0.01743
fisheries_net 0.9713 0.5721 0.1025 0.2201 0.04787 0.4215 0.9885 0.573 0.7361 0.5728 0.2767 0.9196 0.7104 0.7874 0.2496 0.1576 0.4781 0.7212 0.1906
fisheries_trap 2.878e-08 0.1014 5.265e-06 4.889e-05 0.3305 1.481e-06 0.0002478 0.0006488 0.0008039 0.002278 0.001548 0.0001765 6.138e-05 0.0001419 0.4277 0.05927 0.7393 0.524 0.0798
fisheries_trawl 0.02573 0.07593 0.3644 0.05997 0.2811 0.0134 0.001257 0.0002149 9.741e-07 8.969e-05 3.712e-07 0.0009717 0.001194 2.129e-05 0.0248 0.9286 0.6962 0.09349 0.7425
cumulative_exposure 0.01215 0.3291 0.2723 0.1337 0.7181 0.006716 0.1762 0.003005 1.219e-05 1.843e-06 6.524e-05 0.05463 0.001741 6.932e-05 0.7919 0.6858 0.9505 0.655 0.3225

3. Relationships with benthic communities

The most abundant taxa in our study area are:

  • Density: B.neotena (1969), E. integra (1158), P.grandimana (1092), Nematoda (1044) and M. calcarea (575)
  • Biomass: E. parma (biomass of 531.5), Strongylocentrotus sp. (65.3), N. incisa (58.5), M. calcarea (45.4) and S. groenlandicus (34.3)

The following graphs present the distribution of sampled phyla along index of cumulative exposure, according to density or biomass.

Exposure categories are based on the exposure index: the higher the index, the lower the status. Maximum cumulative exposure is 2.015, and the five categories are from ‘bad’ to ‘high’, with 20 %, 40 %, 60 % or 80 % of the maximum exposure.

By exposure gradient

By exposure categories

Phylum mean density by group
Phylum low bad moderate high good
Annelida 15.2 26.8 40.6 27.4 29
Arthropoda 13.4 39.2 55.3 44.3 1
Cnidaria 0 0 0 0 1
Echinodermata 0.2 3.04 3.5 0.96 105
Mollusca 12 9.92 19.2 12.4 19
Nematoda 0 0.458 6.14 17.2 3
Nemertea 0 0.167 0 0.24 0
Sipuncula 0.4 0.417 0.357 0.14 0
Phylum mean biomass by group
Phylum low bad moderate high good
Annelida 3.2 0.913 2.35 0.621 0.0737
Arthropoda 0.0221 0.0666 0.11 0.173 1e-04
Cnidaria 0 0 0 0 3.36
Echinodermata 0.00436 3.79 2.09 8.96 0.455
Mollusca 1.8 0.517 2.39 1.29 1.14
Nematoda 0 3.75e-05 0.000511 0.00067 3e-04
Nemertea 0 0.0712 0 4.4e-05 0
Sipuncula 0.0168 0.0175 0.00519 0.00891 0

4. Relationships with community characteristics

The following graphs present the distribution of community characteristics along index of cumulative exposure.

4.1. Data manipulation

For the following analyses, independant variables are exposure indices, dependant variables are community characteristics. Variables have been standardized by mean and standard-deviation.

All stations and predictors were selected for the regressions, as we are interested in each of them (following graphs are for information only).

Correlation coefficients between exposure indices
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
aquaculture 1 0.061 -0.355 -0.299 -0.666 -0.696 0.724 0.017 0.319 0.571
city 0.061 1 0.334 0.325 0.131 0.22 -0.304 -0.043 -0.008 -0.366
dredging -0.355 0.334 1 0.961 0.668 0.686 -0.685 0.038 -0.089 -0.475
industry -0.299 0.325 0.961 1 0.691 0.598 -0.662 0.097 0.044 -0.475
sewers -0.666 0.131 0.668 0.691 1 0.65 -0.747 0.165 -0.162 -0.563
shipping -0.696 0.22 0.686 0.598 0.65 1 -0.688 0.037 -0.379 -0.641
fisheries_dredge 0.724 -0.304 -0.685 -0.662 -0.747 -0.688 1 -0.08 0.142 0.514
fisheries_net 0.017 -0.043 0.038 0.097 0.165 0.037 -0.08 1 0.135 -0.071
fisheries_trap 0.319 -0.008 -0.089 0.044 -0.162 -0.379 0.142 0.135 1 0.23
fisheries_trawl 0.571 -0.366 -0.475 -0.475 -0.563 -0.641 0.514 -0.071 0.23 1

4.2. Univariate regressions

We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models).

We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the table below:

Human activity S N B H J
Aquaculture
City
Dredging - + +
Industry
Sewers - - - -
Shipping +
Fisheries: Dredge + +
Fisheries: Net
Fisheries: Trap
Fisheries: Bottom-trawling + -
Adjusted \(R^{2}\) 0.19 0.01 0.01 0.15 0.06

Details of the regressions, with diagnostics and cross-validation, are summarized below.

Richness
## FULL MODEL
## Adjusted R2 is: 0.16
Fitting linear model: S ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.095e-16 0.08829 -4.638e-15 1
aquaculture 0.09202 0.116 0.7934 0.4295
city -0.02657 0.1093 -0.243 0.8085
dredging -0.003504 0.1137 -0.03083 0.9755
industry -0.1208 0.1373 -0.8798 0.3811
sewers -0.1956 0.1402 -1.395 0.1661
shipping 0.1581 0.102 1.55 0.1243
fisheries_dredge 0.2228 0.1016 2.192 0.03078 *
fisheries_net -0.002718 0.0891 -0.0305 0.9757
fisheries_trap 0.04523 0.1011 0.4472 0.6557
fisheries_trawl 0.1314 0.09398 1.398 0.1652 
## RMSE from cross-validation: 45.57618
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.31 1.23 1.28 1.55 1.58 1.15 1.15 1 1.14 1.06 

## REDUCED MODEL
## Adjusted R2 is: 0.19
Fitting linear model: S ~ sewers + shipping + fisheries_dredge + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.262e-16 0.08663 -4.92e-15 1
sewers -0.3081 0.0983 -3.134 0.002246 * *
shipping 0.1338 0.09332 1.434 0.1547
fisheries_dredge 0.2483 0.09596 2.588 0.01105 *
fisheries_trawl 0.1321 0.09064 1.457 0.1481 
## RMSE from cross-validation: 0.9119322
Variance Inflation Factors
  sewers shipping fisheries_dredge fisheries_trawl
VIF 1.13 1.07 1.1 1.04

Density
## FULL MODEL
## Adjusted R2 is: -0.03
Fitting linear model: N ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.035e-16 0.09753 2.087e-15 1
aquaculture -0.04097 0.1281 -0.3198 0.7498
city 0.114 0.1208 0.9443 0.3474
dredging -0.1131 0.1256 -0.9005 0.3701
industry -0.2065 0.1517 -1.362 0.1765
sewers 0.2371 0.1548 1.531 0.1289
shipping -0.1087 0.1126 -0.9652 0.3369
fisheries_dredge 0.03383 0.1123 0.3013 0.7638
fisheries_net -0.03855 0.09842 -0.3916 0.6962
fisheries_trap 0.02044 0.1117 0.1829 0.8552
fisheries_trawl 0.05671 0.1038 0.5463 0.5861 
## RMSE from cross-validation: 69.01764
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.31 1.23 1.28 1.55 1.58 1.15 1.15 1 1.14 1.06 

## REDUCED MODEL
## Adjusted R2 is: 0.01
Fitting linear model: N ~ dredging
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.936e-16 0.09571 2.023e-15 1
dredging -0.1414 0.09615 -1.47 0.1444 
## RMSE from cross-validation: 1.00622
Variance Inflation Factors
  dredging
VIF 1

Biomass
## FULL MODEL
## Adjusted R2 is: -0.02
Fitting linear model: B ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.145e-17 0.09734 -6.313e-16 1
aquaculture -0.154 0.1279 -1.204 0.2314
city -0.1777 0.1205 -1.474 0.1437
dredging -0.01418 0.1253 -0.1132 0.9101
industry 0.1864 0.1514 1.231 0.2212
sewers -0.3287 0.1546 -2.127 0.03598 *
shipping -0.1268 0.1124 -1.128 0.2623
fisheries_dredge -0.09693 0.1121 -0.865 0.3892
fisheries_net -0.01053 0.09823 -0.1071 0.9149
fisheries_trap 0.03059 0.1115 0.2744 0.7844
fisheries_trawl -0.01579 0.1036 -0.1524 0.8792 
## RMSE from cross-validation: 1.029382
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.31 1.23 1.28 1.55 1.58 1.15 1.15 1 1.14 1.06 

## REDUCED MODEL
## Adjusted R2 is: 0.01
Fitting linear model: B ~ sewers
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.956e-17 0.09577 -5.175e-16 1
sewers -0.1366 0.09622 -1.419 0.1587 
## RMSE from cross-validation: 0.992684
Variance Inflation Factors
  sewers
VIF 1

Diversity
## FULL MODEL
## Adjusted R2 is: 0.12
Fitting linear model: H ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.732e-16 0.09017 1.92e-15 1
aquaculture 0.1144 0.1184 0.9657 0.3366
city -0.01046 0.1117 -0.09372 0.9255
dredging 0.184 0.1161 1.586 0.1161
industry -0.1202 0.1402 -0.8569 0.3936
sewers -0.2888 0.1432 -2.017 0.04643 *
shipping 0.1179 0.1041 1.132 0.2604
fisheries_dredge 0.1574 0.1038 1.516 0.1327
fisheries_net 0.04753 0.09099 0.5223 0.6026
fisheries_trap -0.02014 0.1033 -0.195 0.8458
fisheries_trawl -0.03562 0.09597 -0.3711 0.7113 
## RMSE from cross-validation: 14.90696
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.31 1.23 1.28 1.55 1.58 1.15 1.15 1 1.14 1.06 

## REDUCED MODEL
## Adjusted R2 is: 0.15
Fitting linear model: H ~ dredging + sewers + fisheries_dredge
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.615e-16 0.08872 1.82e-15 1
dredging 0.1395 0.09707 1.437 0.1538
sewers -0.3605 0.1022 -3.528 0.0006249 * * *
fisheries_dredge 0.1674 0.09678 1.729 0.0867 
## RMSE from cross-validation: 0.9333495
Variance Inflation Factors
  dredging sewers fisheries_dredge
VIF 1.09 1.15 1.09

Evenness
## FULL MODEL
## Adjusted R2 is: 0.01
Fitting linear model: J ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.312e-17 0.09567 -6.597e-16 1
aquaculture 0.04889 0.1257 0.389 0.6981
city 0.03175 0.1185 0.268 0.7892
dredging 0.2245 0.1232 1.823 0.07142
industry -0.1216 0.1488 -0.8173 0.4157
sewers -0.1962 0.1519 -1.292 0.1995
shipping -0.00884 0.1105 -0.08001 0.9364
fisheries_dredge 0.04924 0.1101 0.4471 0.6558
fisheries_net 0.04615 0.09655 0.478 0.6337
fisheries_trap -0.08131 0.1096 -0.742 0.4599
fisheries_trawl -0.14 0.1018 -1.375 0.1722 
## RMSE from cross-validation: 86.80069
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.31 1.23 1.28 1.55 1.58 1.15 1.15 1 1.14 1.06 

## REDUCED MODEL
## Adjusted R2 is: 0.06
Fitting linear model: J ~ dredging + sewers + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.642e-17 0.09322 -7.125e-16 1
dredging 0.1702 0.1017 1.673 0.09726
sewers -0.2962 0.1031 -2.872 0.004941 * *
fisheries_trawl -0.1478 0.09602 -1.54 0.1267 
## RMSE from cross-validation: 1.031223
Variance Inflation Factors
  dredging sewers fisheries_trawl
VIF 1.09 1.1 1.03

Annelid density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.09
Fitting generalized (poisson/log) linear model: annelids ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.341 0.01892 176.6 0 * * *
aquaculture 0.02054 0.02303 0.8917 0.3725
city 0.05554 0.02162 2.569 0.01021 *
dredging -0.1322 0.02965 -4.458 8.255e-06 * * *
industry -0.2923 0.0386 -7.573 3.635e-14 * * *
sewers 0.1486 0.03242 4.585 4.539e-06 * * *
shipping 0.04719 0.01867 2.527 0.0115 *
fisheries_dredge -0.07775 0.02503 -3.106 0.001895 * *
fisheries_net -0.06061 0.02284 -2.653 0.007977 * *
fisheries_trap 0.1069 0.01679 6.366 1.944e-10 * * *
fisheries_trawl -0.2405 0.03317 -7.252 4.117e-13 * * * 
## Unbiased RMSE from cross-validation: 36.44478
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.36 1.41 1.29 1.63 1.65 1.14 1.19 1 1.34 1.05 

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.09
Fitting generalized (poisson/log) linear model: annelids ~ city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.341 0.01891 176.6 0 * * *
city 0.05086 0.02099 2.424 0.01537 *
dredging -0.1329 0.0297 -4.473 7.712e-06 * * *
industry -0.2885 0.03834 -7.525 5.269e-14 * * *
sewers 0.1378 0.03006 4.585 4.538e-06 * * *
shipping 0.0434 0.01818 2.387 0.01698 *
fisheries_dredge -0.07202 0.02365 -3.046 0.002321 * *
fisheries_net -0.06083 0.02284 -2.663 0.007746 * *
fisheries_trap 0.1092 0.01661 6.577 4.798e-11 * * *
fisheries_trawl -0.2415 0.03311 -7.295 2.986e-13 * * * 
## Unbiased RMSE from cross-validation: 36.27681
Variance Inflation Factors
  city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.37 1.29 1.62 1.53 1.11 1.15 1 1.32 1.05

Arthropod density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.19
Fitting generalized (poisson/log) linear model: arthropods ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.605 0.01719 209.7 0 * * *
aquaculture -0.1442 0.02529 -5.702 1.184e-08 * * *
city 0.2193 0.01832 11.97 5.197e-33 * * *
dredging -0.1221 0.02332 -5.236 1.641e-07 * * *
industry -0.7072 0.03413 -20.72 2.063e-95 * * *
sewers 0.7842 0.02651 29.58 2.353e-192 * * *
shipping -0.09841 0.01625 -6.056 1.395e-09 * * *
fisheries_dredge 0.1249 0.01394 8.96 3.26e-19 * * *
fisheries_net -0.06479 0.02029 -3.193 0.001407 * *
fisheries_trap -0.0755 0.01685 -4.48 7.451e-06 * * *
fisheries_trawl 0.06447 0.0157 4.107 4.016e-05 * * * 
## Unbiased RMSE from cross-validation: 97.52826
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.24 1.28 1.24 1.97 2.03 1.11 1.12 1 1.22 1.07 

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.19
Fitting generalized (poisson/log) linear model: arthropods ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.605 0.01719 209.7 0 * * *
aquaculture -0.1442 0.02529 -5.702 1.184e-08 * * *
city 0.2193 0.01832 11.97 5.197e-33 * * *
dredging -0.1221 0.02332 -5.236 1.641e-07 * * *
industry -0.7072 0.03413 -20.72 2.063e-95 * * *
sewers 0.7842 0.02651 29.58 2.353e-192 * * *
shipping -0.09841 0.01625 -6.056 1.395e-09 * * *
fisheries_dredge 0.1249 0.01394 8.96 3.26e-19 * * *
fisheries_net -0.06479 0.02029 -3.193 0.001407 * *
fisheries_trap -0.0755 0.01685 -4.48 7.451e-06 * * *
fisheries_trawl 0.06447 0.0157 4.107 4.016e-05 * * * 
## Unbiased RMSE from cross-validation: 95.18765
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.24 1.28 1.24 1.97 2.03 1.11 1.12 1 1.22 1.07

Mollusc density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.19
Fitting generalized (poisson/log) linear model: molluscs ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.464 0.03033 81.25 0 * * *
aquaculture 0.09909 0.0289 3.428 0.000607 * * *
city 0.2318 0.03029 7.651 1.988e-14 * * *
dredging -0.07629 0.04075 -1.872 0.0612
industry 0.2448 0.03505 6.984 2.867e-12 * * *
sewers -0.2919 0.04435 -6.582 4.647e-11 * * *
shipping -0.2821 0.04408 -6.399 1.567e-10 * * *
fisheries_dredge 0.09612 0.01978 4.859 1.179e-06 * * *
fisheries_net 0.06228 0.02529 2.462 0.01381 *
fisheries_trap 0.009764 0.02451 0.3983 0.6904
fisheries_trawl 0.01857 0.02577 0.7208 0.4711 
## Unbiased RMSE from cross-validation: 17.65345
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.32 1.56 1.52 1.52 1.46 1.21 1.13 1.01 1.36 1.06 

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.19
Fitting generalized (poisson/log) linear model: arthropods ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.605 0.01719 209.7 0 * * *
aquaculture -0.1442 0.02529 -5.702 1.184e-08 * * *
city 0.2193 0.01832 11.97 5.197e-33 * * *
dredging -0.1221 0.02332 -5.236 1.641e-07 * * *
industry -0.7072 0.03413 -20.72 2.063e-95 * * *
sewers 0.7842 0.02651 29.58 2.353e-192 * * *
shipping -0.09841 0.01625 -6.056 1.395e-09 * * *
fisheries_dredge 0.1249 0.01394 8.96 3.26e-19 * * *
fisheries_net -0.06479 0.02029 -3.193 0.001407 * *
fisheries_trap -0.0755 0.01685 -4.48 7.451e-06 * * *
fisheries_trawl 0.06447 0.0157 4.107 4.016e-05 * * * 
## Unbiased RMSE from cross-validation: 93.28045
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.24 1.28 1.24 1.97 2.03 1.11 1.12 1 1.22 1.07

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